FF-net: A Target Detection Method Tailored for Mid-to-Late Stages of Forest Fires in Complex Environments

Abstract

To address the suboptimal detection of forest fires in complex environments during the middle and late stages, we have developed a novel object detection algorithm, FF-net (F_Res, Fire Label Assignment). Initially, a novel fire extraction method, F_Res (Fire ResNet), was designed, incorporating an F_fire activation function to enhance the network's feature extraction and nonlinear fitting capabilities. To reduce the complexity of the network while ensuring detection accuracy, a Fire Label Assignment (FLA) method was devised. Ultimately, the Kullback-Leibler (KL) divergence was integrated with the Focal Loss to create a loss function, KLF (Kullback-Leibler Focal Loss), capable of addressing data imbalance and preventing gradient vanishing and explosion. We reconstructed aerial forest fire and flame datasets and, employing 16 evaluative metrics, compared experiments with classical CNN algorithms, SOTA (State-of-the-Art) algorithms, Transformer algorithms, and advanced fire detection algorithms, discovering that the overall detection efficacy of FF-net surpasses that of other algorithms. In terms of precision on the fire dataset, it exceeds the performance of classical CNN algorithms, SOTA algorithms, and Transformer algorithms by 6.46%, 6.40%, and 21.50% respectively. On the flame dataset, the mean accuracy is 2.47% higher than that of the second-ranking algorithm. Subsequently, visualizing the network's performance on both datasets using Grad-CAM once again confirmed the suitability of FF-net for the task of aerial forest fire detection. The source code of the paper is publicly available at: https://github.com/jyyuan666/FF-net.